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All Journal Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research INTEGER: Journal of Information Technology Jurnal Teknoinfo Jurnal Cendikia CCIT (Creative Communication and Innovative Technology) Journal Building of Informatics, Technology and Science JSR : Jaringan Sistem Informasi Robotik Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pengabdian Masyarakat Formosa (JPMF) Jurnal Publika Pengabdian Masyarakat
handoyo widi nugroho
Institut Informatika Dan Bisnis Darmajaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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Journal : Building of Informatics, Technology and Science

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Implementasi Data Mining Dalam Klasifikasi Tingkat Kesenjangan Kompetensi PNS Menggunakan Metode Naive Bayes Kurniawan, Putra; Wasilah, Wasilah; Sutedi, Sutedi; Nugroho, Handoyo Widi
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5641

Abstract

Civil Servants (Aparatur Sipil Negara or ASN) play crucial roles as implementers of public policy, community service providers, and national unifiers. The government's primary focus is on enhancing the quality and efficiency of public services. In the Provincial Government of Lampung, planning for the enhancement of the competencies of Civil Servants (Aparatur Sipil Negara or ASN) has become a current priority activity. This emphasis is due to the absence of reference data for determining competency development for each ASN. The Assessment Center is one method for determining the competency level of Civil Servants (ASN). However, its implementation faces several challenges such as budget constraints, time limitations, and a shortage of assessors. Based on the results of the 2023 Merit System Index assessment by the Civil Service Commission (KASN), it was recommended that mapping and evaluating employee competency gaps can be carried out through the Human Capital Development Plan (HCDP). In its implementation, a self-assessment method using a questionnaire based on the competency dictionary from the Regulation of the Minister of Administrative and Bureaucratic Reform No. 38 of 2017 is used to address the constraints of the assessment center. The questionnaire is specifically targeted at technical civil servants (PNS) in the Lampung Provincial Government. The analysis of this questionnaire data produces a classification of civil servants based on the level of competency gaps (none, low, medium, high). In this study, the classification results are tested using one of the data mining classification techniques, namely the Naïve Bayes method. The objective of this research is to evaluate the performance of the Naïve Bayes algorithm in classifying the levels of competency gaps among civil servants. Based on the research findings, it can be concluded that the classification system for competency gap levels among civil servants in the Lampung Province Government can be modeled. The testing of the model, which implemented the Naïve Bayes classification method using RapidMiner tools on the research dataset, achieved an accuracy rate of 98.02%. The conclusion is that the Naïve Bayes algorithm performs well in classifying the competency gap levels among civil servants. With the achieved accuracy level, the resulting classifications can be utilized by the Lampung Provincial Government in planning the development needs of civil servant competencies
A Prediksi Rekomendasi Pemilihan Kejuruan pada Sekolah Menengah Kejuruan Menggunakan Perbandingan Metode Decision Tree C4.5 dan Naïve Bayes Windari, Ratih; Nugroho, Handoyo Widi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6928

Abstract

SMK Negeri 4 Bandar Lampung faces challenges in assisting students in selecting a major that aligns with their potential, interests, and abilities. The decision-making process for choosing a major is often influenced by subjective factors that lack transparency and may not be entirely accurate. Therefore, a system is needed to provide more accurate and objective recommendations. This study develops a predictive system for major selection at SMK Negeri 4 Bandar Lampung using two methods: the Decision Tree C4.5 algorithm and the Naïve Bayes algorithm. The system utilizes seven key attributes as predictive variables, including mathematics scores, English scores, science (IPA) scores, Indonesian language scores, academic achievements, participation in extracurricular activities, and color blindness condition. The study findings indicate that the C4.5 algorithm achieves an accuracy of 84.46%, whereas the Naïve Bayes algorithm outperforms it with an accuracy of 92.23%. This suggests that the Naïve Bayes algorithm is more effective for this application. Nevertheless, both methods still have limitations that can be improved through parameter optimization and more in-depth data processing. The implementation of this data-driven system is expected to enhance the efficiency of providing more relevant major recommendations at SMK Negeri 4 Bandar Lampung and serve as an inspiration for other schools to adopt similar approaches to improve education quality.
Co-Authors - Nurjoko Abdi Darmawan Abdul Lathif Sampurna, Abdul Lathif Agung Purwanto Alexander, Ganesis Andani, Bethania Andani, Mia Andrean Danofic Anwar, Mashuril Besti Lilyana, Besti Destanto, Tri Effendi, Mukhammad Khoirul Febri Suganda Goesderilidar Hary Sabita Idris Asmuni Iskandar, Muhammad Yashlan ismail, Rendy Kristyanto, Andi Kurniawan, Putra M Said Hasibuan Muhamad Muslihudin Muhammad Nur Ikhsanto, Muhammad Nur Mukhlisin Mukhlisin Omega, Jessica Anatasya Riko Herwanto Rini Nurlistiani Sadimin Sadimin Sefriyanto, Eka Siti Mukodimah Sriyanto Sriyanto Susanti, Yus Sutedi Sutedi Triloka, Joko Wasilah Wasilah Windari, Ratih Y. Suhendro Yan Aditiya Pratama Yulian, Hengky Yuniwati Yuniwati, Yuniwati Yusuf, Suhendro Zatika, Dinda Anna